From the Carnegie Foundation
math.mtsac.edu/statway/lesson_3.3.1_version1.5A
Slide 3
Observed y MINUS predicted y
Slide 4
Determines the effectiveness of the regression model
Slide 5
A scatterplot of Residuals vs. X
Slide 6
If the model is appropriate, then the plot will have a random
scatter. If another model is necessary, the plot will have a
pattern. Pattern = Problem
Slide 7
Slide 8
Determine, just by visual inspection, if the linear model is
appropriate or inappropriate.
Slide 9
Slide 10
1. Does their appear to be a pattern in the residual plot? Yes,
quadratic. 2. Does this support your original guess? You must now
see that a linear model does NOT fit this data.
Slide 11
Slide 12
1. Does their appear to be a pattern in the residual plot? Yes,
it fans out as x increases. 2. Does this support your original
guess? You must now see that a linear model does NOT fit this
data.
Slide 13
Slide 14
1. Does their appear to be a pattern in the residual plot? Yes,
it looks quadratic. 2. Does this support your original guess? This
was very tricky. The scale was very small. You must now see that a
linear model does NOT fit this data.
Slide 15
Slide 16
1. Does their appear to be a pattern in the residual plot? Yes,
it seems decrease as x increases. 2. Does this support your
original guess? This was tricky. You must now see that a linear
model does NOT fit this data.
Slide 17
Total Time (minutes) Total Distance (miles) Predicted Total
Distance Residuals (observed predicted) 325154.4-3.4 193031.9 2847
3656 1727 2335 4165 2241 3773 2854
Slide 18
Total Time (minutes) Total Distance (miles) Predicted Total
Distance Residuals (observed predicted) 325154.4-3.4 193031.9 -1.9
2847 47.5-0.5 3656 61.3-5.3 1727 28.5-1.5 2335 38.8-3.8 4165 70.0-5
2241 37.13.9 3773 63.19.9 2854 47.56.5
